The present invention relates to a system for source coding of an image which is designed for data compression, and more particularly, to an orthogonal transform coding of images.
A two-dimensional full color still image is made up of a huge volume of information, but the image data carry a pronounced redundancy, which it is possible to eliminate.
A conventional way of such image compression converts each pixel value to spatial frequency components by applying orthogonal transform, such as Fourier transform, Hadamard transform, Haar transform, cosine transform, or Karhunen- Loeve transform (KL transform), all for eliminating redundancy from image data. Since, in such an image compression method, energy concentrates on a specific spatial frequency component as a result of the transform mentioned above, the data can be reduced by allocating a large number of bits to a spatial frequency component with a large amount of energy and a small number of bits to a spatial frequency component with a small amount of energy. FIG. 11 illustrates in a block diagram a coding device for carrying out an orthogonal transform coding of images wherein the bit allocation as mentioned above is practiced.
The image coding device of FIG. 11 operates as follows. Full-color still image data (for example, data in three primary colors--red (R), green (G), and blue (B)--and with a size N.times.N) are transformed into luminance signals (Y) and color-difference signals (I, Q) at a preprocessor 1. This transform into a YIQ system is done for the following reason: whereas an RGB system has a large degree of redundancy because of high correlations between color components, the use of an orthogonal system in the YIQ system reduces the redundancy considerably because of low correlations between the luminance and the color difference; moreover, since, in a YIQ system, man's vision is much less sensitive to a lack of precision in I and Q components than in Y components, thus the characteristics of man's vision permit the I and Q components to be coded in a rough mode and the coding, as a whole, can be reduced in volume accordingly. An RGB system is transformed into its corresponding YIQ system in accordance with the following formula. ##EQU1##
The data transformed into a YIQ system are then transformed into spatial frequency components having conversion axes, which are independent of each other, at an orthogonal transform circuit 2. The orthogonally transformed image data, thereby obtained, are received by an encoder 3 where each frequency component is allocated bits according to the energy given to each frequency component, and then the data are quantized and encoded according to the bits allocated to them and recorded in a recording device 4.
A reconstructed image is reproduced on an image reproducing device 7 by decoding the recorded data in the following manner: the orthogonally transformed data recorded in the recording device 4 are read out and received by a decoder 5, and the data obtained by decoding at the decoder 5 are entered into an inverse transform circuit 6 where the data are inverse transformed to the transform at the orthogonal transform circuit 2. The data are then transformed from the YIQ system to the RGB system which reverses the data to the RGB signals, which reproduce the reconstructed image on the image-reproducing device 7. The inverse transform from the YIQ system to the RGB system is performed in accordance with the following formula: ##EQU2##
It is known that, whereas man's vision is sensitive to low frequency domain of spatial frequencies (i.e., areas of small change, as well as, regions which are relatively flat), man's vision is much less sensitive to errors in high frequency domain (i.e., areas of large change such as edge).
In the practice of the above-mentioned method, however, because the number of bits to be allocated to each spatial frequency component is decided based only on the magnitude of the energy of the frequency components, it occurs that a large number of bits are allocated even to high frequency components if their energies are large. In other words, the above method cannot realize an efficient compression of image data.